AIMC Topic: Delivery of Health Care

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A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems.

Scientific reports
Effective financial risk management in healthcare systems requires intelligent decision-making that balances treatment quality with cost efficiency. This paper proposes a novel hybrid framework that integrates reinforcement learning (RL) with knowled...

Attitudes, Perceptions, and Factors Influencing the Adoption of AI in Health Care Among Medical Staff: Nationwide Cross-Sectional Survey Study.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) has demonstrated transformative potential in the health care field; yet, its clinical adoption faces challenges such as inaccuracy, bias, and data privacy concerns. As the primary operators of AI systems, phys...

Revolutionizing clinical decision making through deep learning and topic modeling for pathway optimization.

Scientific reports
Optimizing clinical pathways is pivotal for enhancing healthcare delivery, yet traditional methods are increasingly insufficient in the face of complex, personalized medical demands. This paper introduces an innovative optimization framework that fus...

Machine learning enables legal risk assessment in internet healthcare using HIPAA data.

Scientific reports
This study explores how artificial intelligence technologies can enhance the regulatory capacity for legal risks in internet healthcare based on a machine learning (ML) analytical framework and utilizes data from the health insurance portability and ...

Nurses perceptions and use of artificial intelligence in healthcare.

Scientific reports
The integration of artificial intelligence (AI) in nursing care is an important professional issue. However, few studies have investigated the knowledge, attitudes, application, and acceptance of artificial intelligence in nursing care. This study ai...

Developing an AI Governance Framework for Safe and Responsible AI in Health Care Organizations: Protocol for a Multimethod Study.

JMIR research protocols
BACKGROUND: Artificial intelligence (AI) has the potential to improve health care delivery through enhanced diagnostics, streamlined operations, and predictive analytics. However, health care organizations face substantial challenges in implementing ...

Blockchain framework with IoT device using federated learning for sustainable healthcare systems.

Scientific reports
The Internet of Medical Things (IoMT) sector has advanced rapidly in recent years, and security and privacy are essential considerations in the IoMT due to the extensive scope and implementation of IoMT networks. Machine learning (ML) and blockchain ...

Clinical characteristics and CKD care delivery in African American and American Indian or Alaska Native patients: A real-world cohort study.

BMC nephrology
BACKGROUND: Racially minoritized populations in the United States (US), notably African American (AA) and American Indian/Alaska Native (AI/AN), experience disproportionately higher rates of chronic kidney disease (CKD), diabetes, and hypertension co...

Artificial intelligence for healthcare: restrained development despite impressive applications.

Infectious diseases of poverty
BACKGROUND: Artificial intelligence (AI) remains poorly understood and its rapid growth raises concerns reminiscent of dystopian narratives. AI has shown the capability of producing new medical content and improving management through optimization an...

Predictive estimations of health systems resilience using machine learning.

BMC medical informatics and decision making
Operationalizing resilience in public health systems is critical for enhancing adaptive capacity during crises. This study presents a Machine Learning (ML) -based approach to assess resilience of the health system. Using historical data from Brazilia...